System and method for providing neoantigen immunotherapy information by using artificial-intelligence-model-based molecular dynamics big data

Abstract

Disclosed are a system and a method of predicting neoantigens and immune response induction. The system and the method may verify induction of immunity against neoantigens having high binding affinity by identifying neoantigen candidates through genomic mutations and then predicting the binding affinities of the neoantigen candidates for MHC through molecular dynamics. The method provides neoantigen immunotherapy information for identifying a neoantigen using artificial intelligence (AI)-based molecular dynamics big data, and includes steps of: (A) identifying neoantigen candidates through a genomic mutation; (B) filtering the specificities of the neoantigen candidates for tissue and disease; (C) predicting the in silico binding of the neoantigens to MHC; and (D) calculating and ranking TCR activity.

1 . A method of providing neoantigen immunotherapy information for identifying a neoantigen using artificial intelligence (AI)-based molecular dynamics big data, the method comprising steps of:
 (A) identifying neoantigen candidates through a genomic mutation;   (B) filtering the specificities of the neoantigen candidates for tissue and disease;   (C) predicting the in silico binding of the neoantigens to MHC; and   (D) calculating and ranking TCR activity.   
     
     
         2 . The method of  claim 1 , wherein the genomic mutation is a mutation present in tumor exomes or tumor transcriptomes. 
     
     
         3 . The method of  claim 2 , wherein the genomic mutation is any one of neo-mutations, exposed features or mal-functions, and verification of exome and transcriptome expression is performed by determining over-expression or differential expression in the transcriptome. 
     
     
         4 . The method of  claim 1 , wherein the neoantigen candidates in step (A) comprise any one or more of major clone genes selected from cancer cells, mesenchymal stroma cell (MSC) genes selected from cancer cells, or six HLA types of cancer cells. 
     
     
         5 . The method of  claim 4 , wherein the six HLA types are HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP and HLA-DQ. 
     
     
         6 . The method of  claim 4 , wherein, for selecting the major clone genes from cancer cells, a clone having the largest number of cancer cells is selected as a major clone from cancer cells composed of the major clone and subclones. 
     
     
         7 . The method of  claim 4 , wherein the mesenchymal stroma cell (MSC) genes selected from cancer cells are collected based on somatic mutation of genes expressed in the stroma cells. 
     
     
         8 . The method of  claim 4 , wherein the HLA types of the cancer cells are selected through genomic HLA typing. 
     
     
         9 . The method of  claim 4 , wherein determination of the HLA types of the cancer cells is performed by a method comprising steps of:
 (a1) collecting read sequences of HLA genes;   (a2) aligning the HLA gene read sequences to a human reference genome sequence according to allele types; and   (a3) determining the types of the HLA genes according to the aligned ranks of the HLA genes.   
     
     
         10 . The method of  claim 1 , wherein step (B) is performed by determining a tissue in which the neoantigen candidates are expressed. 
     
     
         11 . The method of  claim 1 , wherein the predicting of the in silico binding in step (C) is performed by calculating in silico binding affinities (IBA) based on the three-dimensional structures of peptides based on somatic mutation of tissue-specific genes, produced through steps (A) and (B), and a selected MHC protein. 
     
     
         12 . The method of  claim 11 , wherein the predicting of the in silico binding in step (C) is performed by producing peptides based on somatic mutation of the specific genes, and calculating the in silico binding affinities (IBA) through docking based on the three-dimensional structures of the produced peptides and the MHC protein. 
     
     
         13 . The method of  claim 11 , wherein the predicting of the in silico binding in step (C) is performed by generating binding models for a number of types of antigens and calculating the energy difference and RMSD difference therebetween. 
     
     
         14 . The method of  claim 11 , wherein the predicting of the in silico binding in step (C) is performed by a method comprising steps of:
 (C1) performing dynamics simulation for MHC-peptide docking complexes;   (C2) generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data;   (C3) calculating the correlation between rmsds through the phi-psi angles and structures;   (C4) calculating the correlation between selected features and each structure rmsd; and   (C5) determining the in silico binding affinities through an AI model based on features generated from MHC-peptide complexes.   
     
     
         15 . The method of  claim 11 , wherein the in silico binding affinity (IBA) is calculated by the ratio of a predicted drug response (ic50) of a mutant gene to a predicted drug response (ic50) of a wildtype gene. 
     
     
         16 . A system of providing neoantigen immunotherapy information for identifying a neoantigen using AI-based molecular dynamics big data, the system being configured to provide the neoantigen immunotherapy information by identifying neoantigen candidates through a genomic mutation, filtering the specificities of the neoantigens for tissue and disease, predicting the in silico binding of the neoantigens to MHC, and then calculating TCR activity. 
     
     
         17 . The system of  claim 16 , wherein the neoantigen candidates comprise any one or more of major clone genes selected from cancer cells, mesenchymal stroma cell (MSC) genes selected from cancer cells, or six HLA types of cancer cells. 
     
     
         18 . The system of  claim 17 , wherein, for selecting the major clone genes from cancer cells, a clone having the largest number of cancer cells is selected as a major clone from cancer cells composed of the major clone and subclones. 
     
     
         19 . The system of  claim 17 , wherein the mesenchymal stroma cell (MSC) genes selected from cancer cells are collected based on somatic mutation of genes expressed in the stroma cells. 
     
     
         20 . The system of  claim 17 , wherein the six HLA types of the cancer cells are selected through genomic HLA typing. 
     
     
         21 . The system of  claim 17 , wherein determination of the HLA types of the cancer cells is performed by a method comprising steps of:
 (a1) collecting read sequences of HLA genes;   (a2) aligning the HLA gene read sequences to a human reference genome sequence according to allele types; and   (a3) determining the types of the HLA genes according to the aligned ranks of the HLA genes.   
     
     
         22 . The system of  claim 16 , wherein the filtering of the specificity of the neoantigen candidates is performed by determining a tissue in which the neoantigen candidates are expressed. 
     
     
         23 . The system of  claim 16 , wherein the predicting of the binding is performed by calculating in silico binding affinities (IBA) based on the three-dimensional structures of produced peptides based on somatic mutation of tissue-specific genes and a selected MHC protein. 
     
     
         24 . The system of  claim 23 , wherein the predicting of the binding is performed by producing peptides based on somatic mutation of the specific genes, and calculating the in silico binding affinities (IBA) through docking based on the three-dimensional structures of the produced peptides and the MHC protein. 
     
     
         25 . The system of  claim 24 , wherein the predicting of the binding is performed by a method comprising steps of:
 (C1) performing dynamics simulation for MHC-peptide docking complexes;   (C2) generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data;   (C3) calculating the correlation between rmsds through the phi-psi angles and structures;   (C4) calculating the correlation between selected features and each structure rmsd; and   (C5) determining the in silico binding affinities through an AI model based on features generated from MHC-peptide complexes.   
     
     
         26 . The system of  claim 23 , wherein the in silico binding affinity (IBA) is calculated by the ratio of a predicted drug response (ic50) of a mutant gene to a predicted drug response (ic50) of a wildtype gene.